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  • choseqid
    replied
    Hi April,

    What exactly did you do with dispTable(cds)[CondA]?

    Thanks.

    Originally posted by aprilw View Post
    Hi All,

    Figures the first time I post a question I end up answering it myself.

    My conditions were not named what I thought they were. I solved this by using dispTable(cds)[CondA]. With the incorrect condition name this evaluated to NA, thus causing the error.

    Sorry for the stupid question, maybe this will help a similarly misguided person!

    Leave a comment:


  • IsBeth
    replied
    My pipeline in DEseq using data without replicates and 2 conditions is not working either. I thought that something was wrong with the dispersion. But no dispersion method works, so...maybe there's a problem with the preprocessing? Anybody got the same problem with DESeq?

    Leave a comment:


  • jeky82
    replied
    I have the same error … but I did not understand how solve it, can you help me?

    Leave a comment:


  • indugun
    replied
    raju

    Originally posted by aprilw View Post
    Hello,

    I'm working with a data set that we have yet to replicate. I'm trying to take a cursory look at the differential expression, but I keep getting an error I don't understand. I don't know if it matters but I'm generating my count tables with HTSeq. Here is the error:

    > table = read.delim(file="~/neuron_rna_seq/hypodermis_table.txt", header=FALSE, stringsAsFactors=TRUE)
    > cds = newCountDataSetFromHTSeqCount(table, directory="~/neuron_rna_seq")
    > cds = estimateSizeFactors( cds )
    > sizeFactors( cds )
    hypodermis hypodermis_unfiltered
    0.3333333 3.0000000
    > cds = estimateDispersions(cds, method="blind", sharingMode="fit-only")
    Error in parametricDispersionFit(means, disps) :
    Parametric dispersion fit failed. Try a local fit and/or a pooled estimation. (See '?estimateDispersions')
    In addition: Warning message:
    glm.fit: algorithm did not converge
    #Because of this I use the local fit
    > cds = estimateDispersions(cds, method="blind", sharingMode="fit-only", fitType="local")
    > results = nbinomTest(cds, "hypodermis", "hypodermis_unfiltered")
    #Error in question
    Error in if (dispTable(cds)[condA] == "blind" || dispTable(cds)[condB] == :
    missing value where TRUE/FALSE needed


    I apologize if this is a basic question, I'm not an experienced user.

    Thanks!
    April
    Useful
    Thank you

    Leave a comment:


  • Pseudoknot
    replied
    It was useful, thanks!

    Leave a comment:


  • aprilw
    replied
    Answer to my own question

    Hi All,

    Figures the first time I post a question I end up answering it myself.

    My conditions were not named what I thought they were. I solved this by using dispTable(cds)[CondA]. With the incorrect condition name this evaluated to NA, thus causing the error.

    Sorry for the stupid question, maybe this will help a similarly misguided person!

    Leave a comment:


  • aprilw
    started a topic Help Using DESeq without replicates

    Help Using DESeq without replicates

    Hello,

    I'm working with a data set that we have yet to replicate. I'm trying to take a cursory look at the differential expression, but I keep getting an error I don't understand. I don't know if it matters but I'm generating my count tables with HTSeq. Here is the error:

    > table = read.delim(file="~/neuron_rna_seq/hypodermis_table.txt", header=FALSE, stringsAsFactors=TRUE)
    > cds = newCountDataSetFromHTSeqCount(table, directory="~/neuron_rna_seq")
    > cds = estimateSizeFactors( cds )
    > sizeFactors( cds )
    hypodermis hypodermis_unfiltered
    0.3333333 3.0000000
    > cds = estimateDispersions(cds, method="blind", sharingMode="fit-only")
    Error in parametricDispersionFit(means, disps) :
    Parametric dispersion fit failed. Try a local fit and/or a pooled estimation. (See '?estimateDispersions')
    In addition: Warning message:
    glm.fit: algorithm did not converge
    #Because of this I use the local fit
    > cds = estimateDispersions(cds, method="blind", sharingMode="fit-only", fitType="local")
    > results = nbinomTest(cds, "hypodermis", "hypodermis_unfiltered")
    #Error in question
    Error in if (dispTable(cds)[condA] == "blind" || dispTable(cds)[condB] == :
    missing value where TRUE/FALSE needed


    I apologize if this is a basic question, I'm not an experienced user.

    Thanks!
    April

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